Optimal training sample partitioning for two-stage adaptive detectors

Adaptive signal detection for scenarios with a limited number of sources of interest and background interferers (less than the number of antenna elements) can be efficiently executed using diagonally loaded covariance matrix estimates, but the resultant detectors are not strictly constant false-alarm rate (CFAR). The loss of ldquoCFARnessrdquo means that the problem of adaptive interference mitigation and the problem of adaptive false-alarm threshold control must be treated separately, yet draw on the same collection of secondary training samples. Here we consider a ldquotwo-stagerdquo adaptive detection scheme that optimally partitions the total sample support T into two sets: T_CME data samples are used to design the adaptive filter (beamformer), then the remaining T_CFAR samples are used to calculate the adaptive scalar false-alarm threshold. We present a comparative analysis of the detection performance of ldquoone-stagerdquo CFAR and ldquotwo-stagerdquo adaptive detectors.